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Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm

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Abstract

The extraction of effective information in visible–near-infrared (VIS-NIR) spectroscopy is crucial and difficult for spectral analysis. In this research, an algorithm of wavelet feature extraction based on the Gaussian kernel function (GKF-WTEF) was developed to suppress the influence of external interference on VIS-NIR spectroscopy and improve the accuracy of quantitative analysis. This algorithm takes the root-mean-square error of the prediction set (RMSEP) of the model, which is established by partial least-squares regression, as the optimization criteria. First, the optimal type of wavelet function, the decomposition level, and the Gauss kernel function central frequency band are determined according to the RMSEP. Second, the Gauss kernel function bandwidth is determined by Newton’s method. Then, the Hadamard product of the Gaussian kernel function and the wavelet coefficient is obtained. Finally, the wavelet coefficients after the Hadamard product can be reconstructed to obtain the spectral data after feature extraction. In order to verify the effectiveness of this algorithm, the difference in the optical parameters of the polyvinyl chloride material container was used as an external interference source. And the spectrum of Intra-lipid and India-ink mixed solution with different concentrations was collected therein. The volume fraction of India-ink in complex mixed solution was quantitatively analyzed by using the RMSEP and the average relative error of the prediction set as the evaluation criteria. The research results demonstrated that the Gaussian-wavelet transform feature extraction algorithm is an effective pretreatment method, it can satisfactorily suppress the influence of external interference on the spectrum, and it can improve the analytical accuracy of VIS-NIR spectroscopy.

© 2019 Optical Society of America

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